Superbugs beware! AI is teaching ancient viruses some new tricks
A random research led to interesting articles about the world of antibiotics and how natural microbes known as bacteriophages with the help of AI can change the future of Medicine
What is Antimicrobial Resistance (AMR)?
Antimicrobial resistance (AMR) poses a significant threat to global health, as microorganisms evolve to resist the effects of bactericidal and bacteriostatic drugs. In 2019, an estimated 4.95 million deaths were linked to AMR, with 1.27 million directly attributable to it.
The UN estimates that by 2050, up to 10 million deaths could be caused by superbugs and associated forms of antimicrobial resistance, matching the annual global death toll of cancer.
In developing countries, the situation is worsened by the uncontrolled distribution of antibiotics for self-medication, even for minor illnesses.
Could a Phage, a natural bacteria killer, become our savior in the future with the aid of AI?
Bacteriophages and Phage Therapy:
Phage therapy utilizes bacteriophages (phages), viruses that specifically target and kill bacteria, to treat infections. Think of them as tiny bacterial assassins! Phages have been around for over a century, but are gaining renewed interest due to the rise of antibiotic-resistant bacteria.
In Favour:
Specificity: Unlike broad-spectrum antibiotics, phages only target specific bacterial strains, minimizing harm to beneficial gut bacteria.
Reduced resistance: Bacteria develop resistance to antibiotics more easily than to phages, potentially offering a longer-lasting solution.
Biodegradability: Phages are natural and readily degrade after use, posing minimal environmental impact.
Synergy with antibiotics: Can be combined with antibiotics for enhanced efficacy.
Not in Favour:
Limited research: Compared to antibiotics, phage therapy research is less extensive, requiring further clinical trials.
Regulatory hurdles: Approval processes for phage-based therapies are still evolving.
Patient-specific treatment: May need tailoring to individual infections, increasing complexity.
Long-term studies needed: More data is required on long-term effects and potential unintended consequences.
How AI is shaping the future of Phage Therapy?
AI is revolutionizing phage therapy by making it more personalized, efficient, and effective. This advancement holds immense promise for overcoming antibiotic resistance and developing new treatment options for various infections.
Key ways AI is helping to improve phage therapy:
1. Identifying the right phages:
Rapid genomic analysis: AI algorithms can analyze vast datasets of bacterial and phage genomes to quickly identify the specific phages most effective against a particular bacterial strain causing an infection. This personalized approach improves treatment success and reduces side effects.
Predicting bacterial evolution: AI can predict how bacteria might evolve resistance to phages, helping researchers develop phages that remain effective even against evolving strains.
2. Developing potent phage cocktails:
Tailored combinations: AI can analyze data on different phages and their interactions to create optimized cocktails targeting specific bacterial communities. This approach increases the effectiveness and reduces the risk of resistance emerging.
In silico simulations: AI can simulate the dynamics of phage-bacteria interactions to identify the most effective combinations and dosages before real-world testing.
3. Streamlining phage discovery and optimization:
Virtual screening: AI can analyze large libraries of phage sequences to identify promising candidates for further development, saving time and resources.
Protein engineering: AI can design mutations in phage proteins to improve their targeting, efficacy, and safety.
4. Optimizing clinical trials and therapy delivery:
Predicting treatment outcomes: AI can analyze patient data and historical phage therapy cases to predict individual responses and personalize treatment plans.
Developing delivery systems: AI can design targeted delivery systems for phages, ensuring they reach specific infection sites and maximize their effectiveness.
What is a PLM(protein language model)?
Sounds interesting right? A protein Large Language Model(LLM) is probably the future of protein sequencing including k-mer frequency.
In natural language processing, current state-of-the-art large language models are trained in an unsupervised manner on gigantic corpora of text. Recently, this approach has been used to train protein language models (PLMs) on billions of protein sequences to learn real number vector representations of amino acids. PLMs capture the physicochemical properties of amino acids and can resolve protein structural and functional information from sequence input alone.
A National Library of Medicine article explains the advancement of sequencing with the development of PLM and many more custom AI models.
Other Popular ML algorithms in the research history -
Convolutional Neural Network, Decision Tree, Extremely Randomized Tree, Graph Convolutional Network, K-Nearest Neighbors, Naïve Bayes, Random Forest, Support Vector Machine and many more
A Phage Host Prediction Model Architecture with PLM -
Source Ref:
Market outlook of Phage Therapy?
According to multiple reports detailing the market size of phage therapy, projections indicate a range between USD 159.36 million and USD 176.75 million by 2024. These estimations point towards a steadily expanding market, with an anticipated compound annual growth rate (CAGR) of approximately 10.91%. Further detailed analysis is necessary to gain a comprehensive understanding of the market's dynamics.
My Thoughts and Takeaways:
The issue of Antimicrobial Resistance (AMR) appears increasingly inevitable and is becoming a significant global concern. Even having a basic understanding of this problem is invaluable.
The advancement of technology is poised to revolutionize the field of medicine, promising numerous benefits for society. Embracing these technological advancements will undoubtedly lead to significant improvements in healthcare and overall well-being.
I concur that grasping these concepts is vital, and I welcome the chance to exchange perspectives on the subject.
Notable sources to know more in-depth -